Project memo
Team Weather: Colin, Kailer, Jennifer
Data Clean Up Steps for Overall Data
Step 0: Load Libraries
library(tidyverse)
library(broom)
library(lubridate)
library(RColorBrewer)
library(leaflet)
library(crosstalk)
library(htmltools)
library(sf)
library(mapdata)
library(tigris)Step 1: Read Individual CSV Files
Our data came split into separate files by year. Here, we read each of these files, and remove the columns we do not plan on using. These are set to not run because the csv files are too large to easily push to GitHub, and Posit would frequently crash trying to read them.
StormEvents_2010 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2010_c20250520.csv")
StormEvents_2010 <- StormEvents_2010|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2010 <- StormEvents_2010 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2011 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2011_c20250520.csv")
StormEvents_2011 <- StormEvents_2011|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2011 <- StormEvents_2011 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2012 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2012_c20250520.csv")
StormEvents_2012 <- StormEvents_2012|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2012 <- StormEvents_2012 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2013 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2013_c20250520.csv")
StormEvents_2013 <- StormEvents_2013|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2013 <- StormEvents_2013 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2014 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2014_c20250520.csv")
StormEvents_2014 <- StormEvents_2014|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2014 <- StormEvents_2014 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2015 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2015_c20250818.csv")
StormEvents_2015 <- StormEvents_2015|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2015 <- StormEvents_2015 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2016 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2016_c20250818.csv")
StormEvents_2016 <- StormEvents_2016|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2016 <- StormEvents_2016 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2017 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2017_c20250520.csv")
StormEvents_2017 <- StormEvents_2017|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2017 <- StormEvents_2017 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2018 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2018_c20250520.csv")
StormEvents_2018 <- StormEvents_2018|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2018 <- StormEvents_2018 |>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2019 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2019_c20250520.csv")
StormEvents_2019 <- StormEvents_2019|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2019 <- StormEvents_2019|>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)StormEvents_2020 <- read_csv("../data/ignore/StormEvents_details-ftp_v1.0_d2020_c20250702.csv")
StormEvents_2020 <- StormEvents_2020|>
filter(EVENT_TYPE %in% c("Avalanche","Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Tsunami","Wildfire"))
StormEvents_2020 <- StormEvents_2020|>
select(-CZ_TYPE,-CZ_NAME,-TOR_F_SCALE, -TOR_LENGTH,-TOR_WIDTH,-TOR_OTHER_WFO,-TOR_OTHER_CZ_STATE,-TOR_OTHER_CZ_FIPS,-TOR_OTHER_CZ_NAME,-BEGIN_RANGE,-BEGIN_AZIMUTH,-BEGIN_LOCATION,-END_RANGE,-END_AZIMUTH,-END_LOCATION,-EPISODE_NARRATIVE,-EVENT_NARRATIVE,-DATA_SOURCE,-WFO,-YEAR)Step 2: Combine into one CSV file
Next, we took all these individual CSVs and combined them into one. This is also set to not run since the original CSVs are not loaded.
storms <- rbind(StormEvents_2010,
StormEvents_2011,
StormEvents_2012,
StormEvents_2013,
StormEvents_2014,
StormEvents_2015,
StormEvents_2016,
StormEvents_2017,
StormEvents_2018,
StormEvents_2019,
StormEvents_2020)
write_csv(storms, file="../data/storms2.csv")# reload CSV because above chunks are eval=FALSE to avoid crashing Posit.
storms <- read_csv("../data/storms2.csv")Step 3: General Data Cleaning
To put the dates in a more usable format, we split up the format from year month into two columns, year and month.
storms <- storms|>
separate(
col = BEGIN_YEARMONTH,
into = c("BEGIN_YEAR", "BEGIN_MONTH"),
sep = 4,
convert = TRUE
)We also change the date format into one that can be graphed with ggplot without any gaps.
storms <- storms|>
mutate(BEGIN_DATE = as_date(BEGIN_DATE_TIME))Plots
Plot 1: Frequency of storms by year
Data cleanup steps specific to plot 1
We create a new dataframe for this plot, and count the number of storms per year.
storms_by_year <- storms|>
count(BEGIN_YEAR, name = "n_events")Final Plot 1
ggplot(storms_by_year, aes(x = BEGIN_YEAR, y = n_events)) +
geom_line(linewidth = 1) +
geom_point(size = 2) +
labs(
title = "Frequency of Extreme Weather Events (2010–2020)",
subtitle = "Filtered to major natural disasters",
x = "Year",
y = "Number of Events"
) +
theme_minimal()+
scale_x_continuous(breaks = c(2010,2012,2014,2016,2018,2020))
#ggsave("storms_by_year.png")Plot 2: Frequency of storms and fatalities by year
Data cleaning for Plot 2
First, we sum the indirect and direct injuries and deaths into one column, casualties, and merge that data frame with the storms by year dataset.
storms_by_year <- storms %>%
count(BEGIN_YEAR, name = "n_events")
storm_casualties <- storms |>
rowwise(EVENT_ID)|>
mutate(CASUALTIES = sum(c(INJURIES_DIRECT, INJURIES_INDIRECT, DEATHS_DIRECT, DEATHS_INDIRECT)))|>
group_by(BEGIN_YEAR)|>
summarise(casualties_year = sum(CASUALTIES))casualties_storms <- left_join(storm_casualties, storms_by_year)## Joining with `by = join_by(BEGIN_YEAR)`To add in the total damage cost, we must reformat the way the cost is written. In the dataset, cost is abbreviated with K for thousand, and values with no damage are a mix between 0.00K and NA. Here, we rewrite the cost to a usable format and set all NA and 0.00K values to 0. Then, we join the casualty and cost dataframes.
#Replace NA with 0
storm_damages <- storms
storm_damages$DAMAGE_PROPERTY[is.na(storm_damages$DAMAGE_PROPERTY)] <- 0
storm_damages$DAMAGE_CROPS[is.na(storm_damages$DAMAGE_CROPS)] <- 0# Convert crop damage to numeric
storm_damages$CROPS_NUM <- numeric(length(storm_damages$DAMAGE_CROPS))
k_rows <- grepl("K", storm_damages$DAMAGE_CROPS)
storm_damages$CROPS_NUM[k_rows] <- as.numeric(gsub("K", "", storm_damages$DAMAGE_CROPS[k_rows])) / 100# Convert property damage to numeric
storm_damages$PROP_NUM <- numeric(length(storm_damages$DAMAGE_PROPERTY))
k_rows <- grepl("K", storm_damages$DAMAGE_PROPERTY)
storm_damages$PROP_NUM[k_rows] <- as.numeric(gsub("K", "", storm_damages$DAMAGE_PROPERTY[k_rows])) / 100#Find total damage cost per year
storm_damages <- storm_damages|>
rowwise(EVENT_ID)|>
mutate(DAMAGES = sum(c(PROP_NUM, CROPS_NUM)))|>
group_by(BEGIN_YEAR)|>
summarise(damage_year = sum(DAMAGES))
casualties_storms <- left_join(casualties_storms, storm_damages)## Joining with `by = join_by(BEGIN_YEAR)`Final Plot 2
ggplot(casualties_storms, aes(BEGIN_YEAR)) +
geom_line(aes(y = n_events, color = "orange")) +
geom_line(aes(y = casualties_year, color = "red"))+
geom_line(aes(y = damage_year, color = "green"))+
geom_point(aes(y = n_events, color = "orange")) +
geom_point(aes(y = casualties_year, color = "red"))+
geom_point(aes(y = damage_year, color = "green"))+
labs(
title = "Frequency of extreme weather events, cost, and injuries (2010–2020)",
subtitle = "Filtered to major natural disasters",
x = "Year",
y = "Frequency",
color = ""
) +
theme_minimal()+
scale_color_manual(
values = c("orange" = "black", "red" = "purple", "green" = "#00cd00"),
labels = c("orange" = "# of storms",
"red" = "Injures + Fatalities",
"green" = "Damages (in $100k)"))+
scale_x_continuous(breaks = c(2010,2012,2014,2016,2018,2020))
#ggsave("storms_injuries_cost.png")Plot 3: Final Plot 3
Data Cleaning for Plot 3
Here, we count
storms_by_type_year <- storms |>
count(BEGIN_YEAR, EVENT_TYPE, name = "n_events")|>
filter(EVENT_TYPE %in% c("Blizzard","Drought","Flood","Flash Flood","Excessive Heat","Tornado","Tropical Storm","Wildfire"))Final Plot 3
ggplot(storms_by_type_year,
aes(x = BEGIN_YEAR,
y = n_events,
fill = EVENT_TYPE)) +
geom_area(alpha = 0.8, position = "stack") +
labs(
title = "Extreme Weather Events by Storm Type (2010–2020)",
subtitle = "Stacked area shows how different storm types contribute to total events",
x = "Year",
y = "Number of Events",
fill = "Storm type"
) +
theme_minimal() +
scale_x_continuous(breaks = c(2010, 2012, 2014, 2016, 2018, 2020)) +
scale_fill_brewer(palette = "Set2")
#ggsave("storms_by_type.png")Plot 4: ___________
#crosstalk code from https://stackoverflow.com/questions/62849300/r-leaflet-add-a-range-slider-to-filter-markers-without-shiny
map_storms <- storms|>
filter(EVENT_TYPE %in% c("Tornado"))|>
mutate(id = seq_len(n()))
shared_data <- SharedData$new(map_storms, key = ~id, group = "tornado_events")
#slider <- filter_slider("date", "Year", shared_data, ~BEGIN_YEAR, step = 1, ticks = FALSE, width = "90%", dragRange = FALSE, sep = NULL)
slider <- filter_select("year_select", "Select Year", shared_data, ~BEGIN_YEAR, allLevels = TRUE, multiple = FALSE)
leaflet_map <- leaflet(shared_data) %>%
addProviderTiles(provider = providers$OpenStreetMap) %>%
addCircleMarkers(lng = ~BEGIN_LON,
lat = ~BEGIN_LAT,
popup = ~EVENT_TYPE,
color = "red",
radius = 1,
opacity = 0.6,
group = ~EVENT_TYPE)
tagList(
slider,
leaflet_map)Plot 5: ____
Data Cleaning for Plot 5
First, we filter to just tornado, then filter to only include the relevant columns. Finally, we combine two location identifiers, STATE_FIPS and CZ_FIPS. Those are numeric identifiers for each state and county. On the map, there is a column called GEOID, which is the STATE_FIPS followed by the CZ_FIPS. Combining them here allows us to pinpoint exacly what county each event happened in.
#Take storms, make just tornados, create GEOID from FIPS
T_coords <- storms|>
filter(EVENT_TYPE == "Tornado")|>
select(BEGIN_LAT, BEGIN_LON, STATE, STATE_FIPS, CZ_FIPS)|>
mutate(CZ_FIPS = str_pad(CZ_FIPS, width = 3, side = "left", pad = "0"))|>
mutate(STATE_FIPS = str_pad(STATE_FIPS, width = 2, side = "left", pad = "0"))|>
mutate(GEOID = paste0(STATE_FIPS, CZ_FIPS))#Total tornados per county
tornados_per <- T_coords|>
count(GEOID, name = "T_count")
#create the map
map <- map_data('county')|>
rename(NAME = subregion)
#get data from tigris package abt counties, have GEOID and county name
geoid_df <- counties(state = NULL, cb = TRUE)|>
filter(!(STATEFP %in% c("02",15,03,07,14,43,52,64,68,70,74,81,84,
86,67,89,71,76,95,79,60,66,69,72,78)))|>
select(NAME, GEOID, STATEFP, STATE_NAME)|>
mutate(NAME = tolower(NAME))|>
mutate(STATE_NAME = tolower(STATE_NAME))|>
st_drop_geometry(geoid_df)## | | | 0% | | | 1% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======= | 9% | |======= | 10% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========= | 14% | |========== | 14% | |========== | 15% | |=========== | 15% | |=========== | 16% | |============ | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | 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|=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%# Fix connecticut counties
geoid_df[geoid_df == "western connecticut"] <- "fairfield"
geoid_df[geoid_df == "southeastern connecticut"] <- "new london"
geoid_df[geoid_df == "naugatuck valley"] <- "new haven"
geoid_df[geoid_df == "metropolitain"] <- "fairfield"
geoid_df[geoid_df == "south central connecticut"] <- "new haven"
geoid_df[geoid_df == "lower connecticut river valley"] <- "middlesex"
geoid_df[geoid_df == "northeastern connecticut"] <- "windham"
geoid_df[geoid_df == "capitol"] <- "hartford"
geoid_df[geoid_df == "northwest hills"] <- "litchfield"
#Fix connecticut GEOID
#must also fix geoid in T_coords later too
geoid_df[geoid_df == "09110"] <- "09003"
geoid_df[geoid_df == "09120"] <- "09001"
geoid_df[geoid_df == "09130"] <- "09007"
geoid_df[geoid_df == "09140"] <- "09009"
geoid_df[geoid_df == "09150"] <- "09015"
geoid_df[geoid_df == "09160"] <- "09006"
geoid_df[geoid_df == "09170"] <- "09009"
geoid_df[geoid_df == "09180"] <- "09011"
geoid_df[geoid_df == "09190"] <- "09001"
geoid_df <- unique(geoid_df)
#since many counties have same name, create new state county name
map <- map|>
mutate(CZ_ST = paste(NAME, region, sep = ","))
geoid_df <- geoid_df|>
mutate(CZ_ST = paste(NAME, STATE_NAME, sep = ","))#Joins df w/ geoid + county info with tornado locations
all_coords_geoid <- inner_join(geoid_df, T_coords)## Joining with `by = join_by(GEOID)`## Warning in inner_join(geoid_df, T_coords): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1149 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.# counts the number of tornados per county
coords_geoid <- all_coords_geoid|>
count(GEOID, sort = TRUE)
#adds the number of tornados per county back to full df
all_coords_geoid <- inner_join(all_coords_geoid, coords_geoid)|>
select(GEOID, CZ_ST, n)## Joining with `by = join_by(GEOID)`map_n <- inner_join(all_coords_geoid, map)## Joining with `by = join_by(CZ_ST)`## Warning in inner_join(all_coords_geoid, map): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 1573 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.#from https://jtr13.github.io/cc19/different-ways-of-plotting-u-s-map-in-r.html
ggplot(data=map_n, aes(x=long, y=lat, fill=n, group = group))+
geom_polygon(color = "black", linewidth = 0.1) +
guides(fill=FALSE)## Warning: The `<scale>` argument of `guides()` cannot be `FALSE`. Use "none" instead as
## of ggplot2 3.3.4.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.